Data Analysis
Expert in diving deep into data to uncover trends, detect anomalies, and generate actionable insights that drive strategic decisions.
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About Bhavesh Chainani
Data Scientist with 4+ years of professional experience in data science and analytics, adept at leveraging advanced analytical techniques and state-of-the-art technologies to drive business growth and enhance operational efficiency. Skilled in translating complex data into actionable insights for multiple stakeholders and leading end-to-end projects that deliver impactful solutions.
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Proficiencies
- Python (PyTorch, Keras, TensorFlow, Scikit-Learn, Django, Flask, PySpark)
- MLOps (MLFlow, DataRobot)
- AWS (S3, Athena, Glue, SageMaker, Quicksight, Redshift, MWAA, Lambda, API Gateway)
- SQL (MySQL, PostgreSQL, Hive, ElasticSearch, Teradata, Hadoop, MongoDB)
- Data Visualisation (Tableau, Power BI)
- Docker
- Kubernetes
- Java
- JavaScript (ReactJS, NodeJS, NextJS)
- Rest API
- R / R Studio
- MATLab
Data Science
Skilled in leveraging advanced analytics and machine learning to predict outcomes, optimize processes, and gain a competitive edge.
Data Visualization
Proficient in transforming complex data into clear, interactive visuals and dashboards for enhanced understanding and effective communication.
Technical Writing
Experienced in creating clear and precise documentation to ensure technical concepts and data projects are comprehensively understood.
Web Development
Skilled in designing and building intuitive web interfaces that effectively present data solutions, balancing functionality and aesthetics.
Collaboration
Adept at working with teams to integrate data solutions into business processes, supporting both short-term projects and long-term goals.
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Projects
LangChain Analysis
This project demonstrates the use of LangChain to develop and implement language models for various tasks such as text generation, summarization, and chatbots. A practical example includes creating a sample restaurant menu where LangChain interacts with user queries based on cuisine preferences, generating relevant menu items to showcase its capabilities in understanding and responding to user needs.
Topic Modelling
A comprehensive topic modeling analysis for English, Russian, and Italian texts using Latent Dirichlet Allocation (LDA). This project involves preprocessing multilingual datasets, implementing LDA to uncover hidden topics, and analyzing the results to provide insights into the prevalent themes across different languages.
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